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A novel intrusion detection model for the CAN bus packet of in-vehicle network based on attention mechanism and autoencoder

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Digital Communications and Networks 2023年第1期9卷 14-21页

作者： Pengcheng Wei Bo Wang Xiaojun Dai Li Li Fangcheng He School of Mathematics and Information Engineering Chongqing University of EducationChongqingChina School of Automation Chongqing University of Posts and TelecommunicationsChongqingChina Department of Modern Service Chongqing Energy Industry Technician CollegeChongqingChina College of Foreign Languages Literature Chongqing University of EducationChongqingChina

The attacks on in-vehicle Controller Area Network(CAN)bus messages severely disrupt normal communication between ***,researches on intrusion detection models for CAN have positive business value for vehicle security,and the intrusion detection technology for CAN bus messages can effectively protect the invehicle network from unlawful *** machine learning-based models are unable to effectively identify intrusive abnormal messages due to their inherent ***,to address the shortcomings of the previous machine learning-based intrusion detection technique,we propose a novel method using Attention Mechanism and autoencoder for Intrusion Detection(AMAEID).The AMAEID model first converts the raw hexadecimal message data into binary format to obtain better *** the AMAEID model encodes and decodes the binary message data using a multi-layer denoising autoencoder model to obtain a hidden feature representation that can represent the potential features behind the message data at a deeper ***,the AMAEID model uses the attention mechanism and the fully connected layer network to infer whether the message is an abnormal message or *** experimental results with three evaluation metrics on a real in-vehicle CAN bus message dataset outperform some traditional machine learning algorithms,demonstrating the effectiveness of the AMAEID model.

关键词： Controller area network bus packet In-vehicle network Attention mechanism autoencoder

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Enhancing Deep Learning-Based Channel Estimation: A Novel autoencoder-Based Approach

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2025年第5期36卷

作者： Fola, Ephrem Luo, Chunbo Luo, Yang Jiang, Xiangyuan Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China Shandong Univ Inst Marine Sci & Technol Qingdao Peoples R China

Deep-learning (DL) methods have shown promising performance in pioneering studies on orthogonal frequency division multiplexing (OFDM) channel estimation challenges. Unlike typical DL-based channel estimation methods that mainly rely on separate real and imaginary inputs while ignoring the inherent correlation between the two streams, this paper proposes AE-DENet, a novel autoencoder (AE)-based data enhancement network to achieve robust channel estimation for OFDM systems. AE-DENet employs the classic least square (LS) channel estimation as input and proposes a data enhancement method to extract the interaction features from the real and imaginary parts of the complex channel estimation matrix, which are joined with the original real and imaginary streams to generate an enhanced input for better channel inference. Experimental findings in terms of the mean square error (MSE) results for a range of representative DL-based channel estimation methods demonstrate that the proposed AE-DENet-enhanced channel estimation framework achieves state-of-the-art channel estimation performance with trivial added computational complexity. Furthermore, the input dimensions of the DL-based channel estimation models can be adaptively adjusted to accommodate the dimension of the enhanced LS input. The proposed approach is also shown to be robust to channel variations and high user mobility.

关键词： AE-DENet autoencoder channel estimation least square estimation

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autoencoder-based defect detection in PVC profile manufacturing

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INTERNATIONAL JOURNAL OF MANUFACTURING RESEARCH 2024年第2期19卷 119-144页

作者： Aslan, Ahmet Zahit Onal, Sinan Southern Illinois Univ Dept Ind Engn 61 Harping Dr Box 1805 Edwardsville IL 62026 USA

This study develops an automatic defect detection system for polyvinyl chloride (PVC) profile manufacturing, addressing inefficiencies in manual inspection. It compares the proposed autoencoder model with other well-known unsupervised deep-learning methods, including GANomaly, f-AnoGAN, and the student-teacher network, for defect detection during extrusion. Utilising a defective PVC profile dataset, the study generates anomaly heat maps through reconstruction errors and assesses model performance using the area under the receiver operating characteristic (ROC) curve. The proposed autoencoder model is found to be optimal for this dataset, offering a balance between efficiency and accuracy. These findings have significant implications for enhancing quality control and reducing defects in PVC manufacturing, with potential applicability in other industrial settings.

关键词： automated defect detection polyvinyl chloride PVC profiles unsupervised deep learning models autoencoder quality control in manufacturing

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An autoencoder-based feature level fusion for speech emotion recognition

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Digital Communications and Networks 2024年第5期10卷 1341-1351页

作者： Peng Shixin Chen Kai Tian Tian Chen Jingying National Engineering Research Center for E-Learning National Engineering Laboratory for Educational Big DataCentral China Normal UniversityHubei430079China

Although speech emotion recognition is challenging,it has broad application prospects in human-computer *** a system that can accurately and stably recognize emotions from human languages can provide a better user ***,the current unimodal emotion feature representations are not distinctive enough to accomplish the recognition,and they do not effectively simulate the inter-modality dynamics in speech emotion recognition *** paper proposes a multimodal method that utilizes both audio and semantic content for speech emotion *** proposed method consists of three parts:two high-level feature extractors for text and audio modalities,and an autoencoder-based feature *** audio modality,we propose a structure called Temporal Global Feature Extractor(TGFE)to extract the high-level features of the timefrequency domain relationship from the original speech *** that text lacks frequency information,we use only a Bidirectional Long Short-Term Memory network(BLSTM)and attention mechanism to simulate an intra-modal *** these steps have been accomplished,the high-level text and audio features are sent to the autoencoder in parallel to learn their shared representation for final emotion *** conducted extensive experiments on three public benchmark datasets to evaluate our *** results on Interactive Emotional Motion Capture(IEMOCAP)and Multimodal EmotionLines Dataset(MELD)outperform the existing ***,the results of CMU Multi-modal Opinion-level Sentiment Intensity(CMU-MOSI)are ***,experimental results show that compared to unimodal information and autoencoderbased feature level fusion,the joint multimodal information(audio and text)improves the overall performance and can achieve greater accuracy than simple feature concatenation.

关键词： Attention mechanism autoencoder Bimodal fusion Emotion recognition

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A Bi-population Cooperative Optimization Algorithm Assisted by an autoencoder for Medium-scale Expensive Problems

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1952-1966页

作者： Meiji Cui Li Li MengChu Zhou Jiankai Li Abdullah Abusorrah Khaled Sedraoui IEEE the School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China the Department of Electronics and Information Engineering Tongji UniversityShanghai 201804China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

This study presents an autoencoder-embedded optimization(AEO)algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems(MEPs).A huge search space can be compressed to an informative lowdimensional space by using an autoencoder as a dimension reduction *** search operation conducted in this low space facilitates the population with fast convergence towards the *** strike the balance between exploration and exploitation during optimization,two phases of a tailored teaching-learning-based optimization(TTLBO)are adopted to coevolve solutions in a distributed fashion,wherein one is assisted by an autoencoder and the other undergoes a regular evolutionary ***,a dynamic size adjustment scheme according to problem dimension and evolutionary progress is proposed to promote information exchange between these two phases and accelerate evolutionary convergence *** proposed algorithm is validated by testing benchmark functions with dimensions varying from 50 to *** indicated in our experiments,TTLBO is suitable for dealing with medium-scale problems and thus incorporated into the AEO framework as a base *** with the state-of-the-art algorithms for MEPs,AEO shows extraordinarily high efficiency for these challenging problems,t hus opening new directions for various evolutionary algorithms under AEO to tackle MEPs and greatly advancing the field of medium-scale computationally expensive optimization.

关键词： autoencoder dimension reduction evolutionary algorithm medium-scale expensive problems teaching-learning-based optimization

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Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第8期10卷 3035-3043页

作者： Wang, Wei Zhao, Mengxue Wang, Jigang Beijing Jiaotong Univ Beijing Key Lab Secur & Privacy Intelligent Trans Beijing Peoples R China Sci & Technol Elect Informat Control Lab Chengdu Sichuan Peoples R China ZTE Corp Chengdu Sichuan Peoples R China

Android security incidents occurred frequently in recent years. To improve the accuracy and efficiency of large-scale Android malware detection, in this work, we propose a hybrid model based on deep autoencoder (DAE) and convolutional neural network (CNN). First, to improve the accuracy of malware detection, we reconstruct the high-dimensional features of Android applications (apps) and employ multiple CNN to detect Android malware. In the serial convolutional neural network architecture (CNN-S), we use Relu, a non-linear function, as the activation function to increase sparseness and dropout to prevent over-fitting. The convolutional layer and pooling layer are combined with the full-connection layer to enhance feature extraction capability. Under these conditions, CNN-S shows powerful ability in feature extraction and malware detection. Second, to reduce the training time, we use deep autoencoder as a pre-training method of CNN. With the combination, deep autoencoder and CNN model (DAE-CNN) can learn more flexible patterns in a short time. We conduct experiments on 10,000 benign apps and 13,000 malicious apps. CNN-S demonstrates a significant improvement compared with traditional machine learning methods in Android malware detection. In details, compared with SVM, the accuracy with the CNN-S model is improved by 5%, while the training time using DAE-CNN model is reduced by 83% compared with CNN-S model.

关键词： Deep learning Convolutional neural network autoencoder Malware detection Android applications

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Combining KNN with autoencoder for Outlier Detection

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Journal of Computer Science & Technology 2024年第5期39卷 1153-1166页

作者： Shu-Zheng Liu Shuai Ma Han-Qing Chen Li-Zhen Cui Jie Ding State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Software Shandong UniversityJinan 250100China Joint SDU-NTU Centre for Artificial Intelligence Research Shandong UniversityJinan 250100China School of Computer Science Jiangsu University of Science and TechnologyZhenjiang 212003China

K-nearest neighbor(KNN)is one of the most fundamental methods for unsupervised outlier detection because of its various advantages,e.g.,ease of use and relatively high ***,most data analytic tasks need to deal with high-dimensional data,and the KNN-based methods often fail due to“the curse of dimensionality”.autoencoder-based methods have recently been introduced to use reconstruction errors for outlier detection on high-dimensional data,but the direct use of autoencoder typically does not preserve the data proximity relationships well for outlier *** this study,we propose to combine KNN with autoencoder for outlier ***,we propose the Nearest Neighbor autoencoder(NNAE)by persevering the original data proximity in a much lower dimension that is more suitable for performing ***,we propose the K-nearest reconstruction neighbors(K NRNs)by incorporating the reconstruction errors of NNAE with the K-distances of KNN to detect ***,we develop a method to automatically choose better parameters for optimizing the structure of ***,using five real-world datasets,we experimentally show that our proposed approach NNAE+K NRN is much better than existing methods,i.e.,KNN,Isolation Forest,a traditional autoencoder using reconstruction errors(autoencoder-RE),and Robust autoencoder.

关键词： outlier detection autoencoder K-nearest neighbor(KNN) unsupervised learning

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Representation learning: serial-autoencoder for personalized recommendation

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Frontiers of Computer Science 2024年第4期18卷 61-72页

作者： Yi ZHU Yishuai GENG Yun LI Jipeng QIANG Xindong WU School of Information Engineering Yangzhou UniversityYangzhou 225127China Key Laboratory of Knowledge Engineering with Big Data(Ministry of Education of the People’s Republic of China) Hefei University of TechnologyHefei 230009China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China

Nowadays,the personalized recommendation has become a research hotspot for addressing information *** this,generating effective recommendations from sparse data remains a ***,auxiliary information has been widely used to address data sparsity,but most models using auxiliary information are linear and have limited *** to the advantages of feature extraction and no-label requirements,autoencoder-based methods have become quite ***,most existing autoencoder-based methods discard the reconstruction of auxiliary information,which poses huge challenges for better representation learning and model *** address these problems,we propose Serial-autoencoder for Personalized Recommendation(SAPR),which aims to reduce the loss of critical information and enhance the learning of feature ***,we first combine the original rating matrix and item attribute features and feed them into the first autoencoder for generating a higher-level representation of the ***,we use a second autoencoder to enhance the reconstruction of the data representation of the prediciton rating *** output rating information is used for recommendation *** experiments on the MovieTweetings and MovieLens datasets have verified the effectiveness of SAPR compared to state-of-the-art models.

关键词： personalized recommendation autoencoder representation learning collaborative filtering

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An On-Chip Learning Neuromorphic autoencoder With Current-Mode Transposable Memory Read and Virtual Lookup Table

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IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2018年第1期12卷 161-170页

作者： Cho, Hwasuk Son, Hyunwoo Seong, Kihwan Kim, Byungsub Park, Hong-June Sim, Jae-Yoon Pohang Univ Sci & Technol Dept Elect & Elect Engn Pohang 790784 South Korea Samsung Elect Hwaseong Si 18448 South Korea

This paper presents an IC implementation of on-chip learning neuromorphic autoencoder unit in a form of rate-based spiking neural network. With a current-mode signaling scheme embedded in a 500 x 500 6b SRAM-based memory, the proposed architecture achieves simultaneous processing of multiplications and accumulations. In addition, a transposable memory read for both forward and backward propagations and a virtual lookup table are also proposed to perform an unsupervised learning of restricted Boltzmann machine. The IC is fabricated using 28-nm CMOS process and is verified in a three-layer network of encoder-decoder pair for training and recovery of images with two-dimensional 16 x 16 pixels. With a dataset of 50 digits, the IC shows a normalized root mean square error of 0.078. Measured energy efficiencies are 4.46 pJ per synaptic operation for inference and 19.26 pJ per synaptic weight update for learning, respectively. The learning performance is also estimated by simulations if the proposed hardware architecture is extended to apply to a batch training of 60 000 MNIST datasets.

关键词： autoencoder deep belief network (DBN) neuromorphic on-chip learning restricted Boltzmann machine (RBM) unsupervised learning

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An evolutionary autoencoder for dynamic community detection

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Science China(Information Sciences) 2020年第11期63卷 219-234页

作者： Zhen WANG Chunyu WANG Chao GAO Xuelong LI Xianghua LI School of Mechanical Engineering and The Center for OPTIMAL Northwestern Polytechnical University College of Computer and Information Science Southwest University School of Computer Science and The Center for OPTIMAL Northwestern Polytechnical University

Dynamic community detection is significant for controlling and capturing the temporal features of networks. The evolutionary clustering framework provides a temporal smoothness constraint for simultaneously maximizing the clustering quality at the current time step and minimizing the clustering deviation between two successive time steps. Based on this framework, some existing methods, such as the evolutionary spectral clustering and evolutionary nonnegative matrix factorization, aim to look for the low-dimensional representation by mapping reconstruction. However, such reconstruction does not address the nonlinear characteristics of networks. In this paper, we propose a semi-supervised algorithm(sE-autoencoder) to overcome the effects of nonlinear property on the low-dimensional representation. Our proposed method extends the typical nonlinear reconstruction model to the dynamic network by constructing a temporal matrix. More specifically, the potential community characteristics and the previous clustering, as the prior information,are incorporated into the loss function as a regularization term. Experimental results on synthetic and realworld datasets demonstrate that the proposed method is effective and superior to other methods for dynamic community detection.

关键词： dynamic networks community detection autoencoder graph embedding

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